# Compute Summary Statistics of zoo Objects Splits a `"zoo"` object into subsets along a coarser index grid, computes summary statistics for each, and returns the reduced `"zoo"` object. ```r ## S3 method for class 'zoo' aggregate(x, by, FUN = sum, ..., regular = NULL, frequency = NULL, coredata = TRUE) ``` ## Arguments - `x`: an object of class `"zoo"`. - `by`: index vector of the same length as `index(x)` which defines aggregation groups and the new index to be associated with each group. If `by` is a function, then it is applied to `index(x)` to obtain the aggregation groups. - `FUN`: a function to compute the summary statistics which can be applied to all subsets. Always needs to return a result of fixed length (typically scalar). - ``...``: further arguments passed to `FUN`. - `regular`: logical. Should the aggregated series be coerced to class `"zooreg"` (if the series is regular)? The default is `FALSE` for `"zoo"` series and `TRUE` for `"zooreg"` series. - `frequency`: numeric indicating the frequency of the aggregated series (if a `"zooreg"` series should be returned. The default is to determine the frequency from the data if `regular` is `TRUE`. If `frequency` is specified, it sets `regular` to `TRUE`. See examples for illustration. - `coredata`: logical. Should only the `coredata(x)` be passed to every `by` group? If set to `FALSE` the full zoo series is used. ## Returns An object of class `"zoo"` or `"zooreg"`. ## Note The `xts` package functions `endpoints`, `period.apply` `to.period`, `to.weekly`, `to.monthly`, etc., can also directly input and output certain `zoo` objects and so can be used for aggregation tasks in some cases as well. ## See Also `zoo` ## Examples ```r ## averaging over values in a month: # x.date is jan 1,3,5,7; feb 9,11,13; mar 15,17,19 x.date <- as.Date(paste(2004, rep(1:4, 4:1), seq(1,20,2), sep = "-")); x.date x <- zoo(rnorm(12), x.date); x # coarser dates - jan 1 (4 times), feb 1 (3 times), mar 1 (3 times) x.date2 <- as.Date(paste(2004, rep(1:4, 4:1), 1, sep = "-")); x.date2 x2 <- aggregate(x, x.date2, mean); x2 # same - uses as.yearmon x2a <- aggregate(x, as.Date(as.yearmon(time(x))), mean); x2a # same - uses by function x2b <- aggregate(x, function(tt) as.Date(as.yearmon(tt)), mean); x2b # same - uses cut x2c <- aggregate(x, as.Date(cut(time(x), "month")), mean); x2c # almost same but times of x2d have yearmon class rather than Date class x2d <- aggregate(x, as.yearmon, mean); x2d # compare time series plot(x) lines(x2, col = 2) ## aggregate a daily time series to a quarterly series # create zoo series tt <- as.Date("2000-1-1") + 0:300 z.day <- zoo(0:300, tt) # function which returns corresponding first "Date" of quarter first.of.quarter <- function(tt) as.Date(as.yearqtr(tt)) # average z over quarters # 1. via "yearqtr" index (regular) # 2. via "Date" index (not regular) z.qtr1 <- aggregate(z.day, as.yearqtr, mean) z.qtr2 <- aggregate(z.day, first.of.quarter, mean) # The last one used the first day of the quarter but suppose # we want the first day of the quarter that exists in the series # (and the series does not necessarily start on the first day # of the quarter). z.day[!duplicated(as.yearqtr(time(z.day)))] # This is the same except it uses the last day of the quarter. # It requires R 2.6.0 which introduced the fromLast= argument. ## Not run: z.day[!duplicated(as.yearqtr(time(z.day)), fromLast = TRUE)] ## End(Not run) # The aggregated series above are of class "zoo" (because z.day # was "zoo"). To create a regular series of class "zooreg", # the frequency can be automatically chosen zr.qtr1 <- aggregate(z.day, as.yearqtr, mean, regular = TRUE) # or specified explicitely zr.qtr2 <- aggregate(z.day, as.yearqtr, mean, frequency = 4) ## aggregate on month and extend to monthly time series if(require(chron)) { y <- zoo(matrix(11:15, nrow = 5, ncol = 2), chron(c(15, 20, 80, 100, 110))) colnames(y) <- c("A", "B") # aggregate by month using first of month as times for coarser series # using first day of month as repesentative time y2 <- aggregate(y, as.Date(as.yearmon(time(y))), head, 1) # fill in missing months by merging with an empty series containing # a complete set of 1st of the months yrt2 <- range(time(y2)) y0 <- zoo(,seq(from = yrt2[1], to = yrt2[2], by = "month")) merge(y2, y0) } # given daily series keep only first point in each month at # day 21 or more z <- zoo(101:200, as.Date("2000-01-01") + seq(0, length = 100, by = 2)) zz <- z[as.numeric(format(time(z), "%d")) >= 21] zz[!duplicated(as.yearmon(time(zz)))] # same except times are of "yearmon" class aggregate(zz, as.yearmon, head, 1) # aggregate POSIXct seconds data every 10 minutes Sys.setenv(TZ = "GMT") tt <- seq(10, 2000, 10) x <- zoo(tt, structure(tt, class = c("POSIXt", "POSIXct"))) aggregate(x, time(x) - as.numeric(time(x)) %% 600, mean) # aggregate weekly series to a series with frequency of 52 per year suppressWarnings(RNGversion("3.5.0")) set.seed(1) z <- zooreg(1:100 + rnorm(100), start = as.Date("2001-01-01"), deltat = 7) # new.freq() converts dates to a grid of freq points per year # yd is sequence of dates of firsts of years # yy is years of the same sequence # last line interpolates so dates, d, are transformed to year + frac of year # so first week of 2001 is 2001.0, second week is 2001 + 1/52, third week # is 2001 + 2/52, etc. new.freq <- function(d, freq = 52) { y <- as.Date(cut(range(d), "years")) + c(0, 367) yd <- seq(y[1], y[2], "year") yy <- as.numeric(format(yd, "%Y")) floor(freq * approx(yd, yy, xout = d)$y) / freq } # take last point in each period aggregate(z, new.freq, tail, 1) # or, take mean of all points in each aggregate(z, new.freq, mean) # example of taking means in the presence of NAs z.na <- zooreg(c(1:364, NA), start = as.Date("2001-01-01")) aggregate(z.na, as.yearqtr, mean, na.rm = TRUE) # Find the sd of all days that lie in any Jan, all days that lie in # any Feb, ..., all days that lie in any Dec (i.e. output is vector with # 12 components) aggregate(z, format(time(z), "%m"), sd) ```

aggregate.zoo function